{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vjnah732ZKsd"
      },
      "source": [
        "# Batch variant scoring"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8DVMMApBqEGT"
      },
      "source": [
        "In this notebook, we demonstrate how to score many variants using AlphaGenome.\n",
        "\n",
        "To prepare numerous variants for scoring, provide the following information as\n",
        "columns in a tab-separated Variant Call Format (VCF) file: - `variant_id`: A\n",
        "unique identifier for each variant. - `CHROM`: Chromosome, specified as a string\n",
        "beginning with 'chr' (e.g., 'chr1'). - `POS`: Integer representing the base pair\n",
        "position (1-based; build hg38 (human) or mm10 (mouse) - see\n",
        "[FAQ](https://www.alphagenomedocs.com/faqs.html#how-do-i-define-a-variant)). -\n",
        "`REF`: The reference nucleotide sequence at the specified position. - `ALT`: The\n",
        "alternate nucleotide sequence at the specified position."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-coxSuBNqDc"
      },
      "source": [
        "``` {tip}\n",
        "Open this tutorial in Google Colab for interactive viewing.\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "executionInfo": {
          "elapsed": 55,
          "status": "ok",
          "timestamp": 1753113298483,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "WA7A7CAEJFmZ"
      },
      "outputs": [],
      "source": [
        "# @title Install AlphaGenome\n",
        "\n",
        "# @markdown Run this cell to install AlphaGenome.\n",
        "from IPython.display import clear_output\n",
        "! pip install alphagenome\n",
        "clear_output()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "executionInfo": {
          "elapsed": 2531,
          "status": "ok",
          "timestamp": 1753113301166,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "nt721WCQBSyH"
      },
      "outputs": [],
      "source": [
        "# @title Setup and imports.\n",
        "\n",
        "from io import StringIO\n",
        "from alphagenome import colab_utils\n",
        "from alphagenome.data import genome\n",
        "from alphagenome.models import dna_client, variant_scorers\n",
        "from google.colab import data_table, files\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "\n",
        "data_table.enable_dataframe_formatter()\n",
        "\n",
        "# Load the model.\n",
        "dna_model = dna_client.create(colab_utils.get_api_key())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "height": 843
        },
        "executionInfo": {
          "elapsed": 10501,
          "status": "ok",
          "timestamp": 1753113311836,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "fNX9Mg80HuPZ",
        "outputId": "1707defc-2066-48c7-fc24-cfd3abc562cd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 4/4 [00:09\u003c00:00,  2.39s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: total number of rows (121956) exceeds max_rows (20000). Falling back to pandas display.\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_scores"
            },
            "text/html": [
              "\n",
              "  \u003cdiv id=\"df-6bf06b5c-580c-4a30-836c-54fbf5012d99\" class=\"colab-df-container\"\u003e\n",
              "    \u003cdiv\u003e\n",
              "\u003cstyle scoped\u003e\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\u003c/style\u003e\n",
              "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
              "  \u003cthead\u003e\n",
              "    \u003ctr style=\"text-align: right;\"\u003e\n",
              "      \u003cth\u003e\u003c/th\u003e\n",
              "      \u003cth\u003evariant_id\u003c/th\u003e\n",
              "      \u003cth\u003escored_interval\u003c/th\u003e\n",
              "      \u003cth\u003egene_id\u003c/th\u003e\n",
              "      \u003cth\u003egene_name\u003c/th\u003e\n",
              "      \u003cth\u003egene_type\u003c/th\u003e\n",
              "      \u003cth\u003egene_strand\u003c/th\u003e\n",
              "      \u003cth\u003ejunction_Start\u003c/th\u003e\n",
              "      \u003cth\u003ejunction_End\u003c/th\u003e\n",
              "      \u003cth\u003eoutput_type\u003c/th\u003e\n",
              "      \u003cth\u003evariant_scorer\u003c/th\u003e\n",
              "      \u003cth\u003etrack_name\u003c/th\u003e\n",
              "      \u003cth\u003etrack_strand\u003c/th\u003e\n",
              "      \u003cth\u003eAssay title\u003c/th\u003e\n",
              "      \u003cth\u003eontology_curie\u003c/th\u003e\n",
              "      \u003cth\u003ebiosample_name\u003c/th\u003e\n",
              "      \u003cth\u003ebiosample_type\u003c/th\u003e\n",
              "      \u003cth\u003etranscription_factor\u003c/th\u003e\n",
              "      \u003cth\u003ehistone_mark\u003c/th\u003e\n",
              "      \u003cth\u003egtex_tissue\u003c/th\u003e\n",
              "      \u003cth\u003eraw_score\u003c/th\u003e\n",
              "      \u003cth\u003equantile_score\u003c/th\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/thead\u003e\n",
              "  \u003ctbody\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e0\u003c/th\u003e\n",
              "      \u003ctd\u003echr3:58394738:A\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr3:57870450-58919026:.\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eATAC\u003c/td\u003e\n",
              "      \u003ctd\u003eCenterMaskScorer(requested_output=ATAC, width=...\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000084 ATAC-seq\u003c/td\u003e\n",
              "      \u003ctd\u003e.\u003c/td\u003e\n",
              "      \u003ctd\u003eATAC-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000084\u003c/td\u003e\n",
              "      \u003ctd\u003eT-cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.003778\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.215740\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e1\u003c/th\u003e\n",
              "      \u003ctd\u003echr3:58394738:A\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr3:57870450-58919026:.\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eATAC\u003c/td\u003e\n",
              "      \u003ctd\u003eCenterMaskScorer(requested_output=ATAC, width=...\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000100 ATAC-seq\u003c/td\u003e\n",
              "      \u003ctd\u003e.\u003c/td\u003e\n",
              "      \u003ctd\u003eATAC-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000100\u003c/td\u003e\n",
              "      \u003ctd\u003emotor neuron\u003c/td\u003e\n",
              "      \u003ctd\u003ein_vitro_differentiated_cells\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e0.012303\u003c/td\u003e\n",
              "      \u003ctd\u003e0.280661\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e2\u003c/th\u003e\n",
              "      \u003ctd\u003echr3:58394738:A\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr3:57870450-58919026:.\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eATAC\u003c/td\u003e\n",
              "      \u003ctd\u003eCenterMaskScorer(requested_output=ATAC, width=...\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000236 ATAC-seq\u003c/td\u003e\n",
              "      \u003ctd\u003e.\u003c/td\u003e\n",
              "      \u003ctd\u003eATAC-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000236\u003c/td\u003e\n",
              "      \u003ctd\u003eB cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.000334\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.080577\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e3\u003c/th\u003e\n",
              "      \u003ctd\u003echr3:58394738:A\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr3:57870450-58919026:.\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eATAC\u003c/td\u003e\n",
              "      \u003ctd\u003eCenterMaskScorer(requested_output=ATAC, width=...\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000623 ATAC-seq\u003c/td\u003e\n",
              "      \u003ctd\u003e.\u003c/td\u003e\n",
              "      \u003ctd\u003eATAC-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000623\u003c/td\u003e\n",
              "      \u003ctd\u003enatural killer cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.009233\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.440600\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e4\u003c/th\u003e\n",
              "      \u003ctd\u003echr3:58394738:A\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr3:57870450-58919026:.\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eATAC\u003c/td\u003e\n",
              "      \u003ctd\u003eCenterMaskScorer(requested_output=ATAC, width=...\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000624 ATAC-seq\u003c/td\u003e\n",
              "      \u003ctd\u003e.\u003c/td\u003e\n",
              "      \u003ctd\u003eATAC-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000624\u003c/td\u003e\n",
              "      \u003ctd\u003eCD4-positive, alpha-beta T cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.010510\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.440600\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e...\u003c/th\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e121951\u003c/th\u003e\n",
              "      \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292423\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292423\u003c/td\u003e\n",
              "      \u003ctd\u003elncRNA\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eRNA_SEQ\u003c/td\u003e\n",
              "      \u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n",
              "      \u003ctd\u003eUBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...\u003c/td\u003e\n",
              "      \u003ctd\u003e.\u003c/td\u003e\n",
              "      \u003ctd\u003epolyA plus RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eUBERON:0036149\u003c/td\u003e\n",
              "      \u003ctd\u003esuprapubic skin\u003c/td\u003e\n",
              "      \u003ctd\u003etissue\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eSkin_Not_Sun_Exposed_Suprapubic\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000052\u003c/td\u003e\n",
              "      \u003ctd\u003e0.069106\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e121952\u003c/th\u003e\n",
              "      \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292430\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292430\u003c/td\u003e\n",
              "      \u003ctd\u003elncRNA\u003c/td\u003e\n",
              "      \u003ctd\u003e+\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eRNA_SEQ\u003c/td\u003e\n",
              "      \u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n",
              "      \u003ctd\u003eUBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...\u003c/td\u003e\n",
              "      \u003ctd\u003e.\u003c/td\u003e\n",
              "      \u003ctd\u003epolyA plus RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eUBERON:0036149\u003c/td\u003e\n",
              "      \u003ctd\u003esuprapubic skin\u003c/td\u003e\n",
              "      \u003ctd\u003etissue\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eSkin_Not_Sun_Exposed_Suprapubic\u003c/td\u003e\n",
              "      \u003ctd\u003e0.004865\u003c/td\u003e\n",
              "      \u003ctd\u003e0.963724\u003c/td\u003e\n",
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              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
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            ],
            "text/plain": [
              "               variant_id           scored_interval  ... raw_score quantile_score\n",
              "0       chr3:58394738:A\u003eT  chr3:57870450-58919026:.  ... -0.003778      -0.215740\n",
              "1       chr3:58394738:A\u003eT  chr3:57870450-58919026:.  ...  0.012303       0.280661\n",
              "2       chr3:58394738:A\u003eT  chr3:57870450-58919026:.  ... -0.000334      -0.080577\n",
              "3       chr3:58394738:A\u003eT  chr3:57870450-58919026:.  ... -0.009233      -0.440600\n",
              "4       chr3:58394738:A\u003eT  chr3:57870450-58919026:.  ... -0.010510      -0.440600\n",
              "...                   ...                       ...  ...       ...            ...\n",
              "121951  chr16:1135446:G\u003eT    chr16:611158-1659734:.  ...  0.000052       0.069106\n",
              "121952  chr16:1135446:G\u003eT    chr16:611158-1659734:.  ...  0.004865       0.963724\n",
              "121953  chr16:1135446:G\u003eT    chr16:611158-1659734:.  ... -0.000141      -0.137554\n",
              "121954  chr16:1135446:G\u003eT    chr16:611158-1659734:.  ...  0.000508       0.363304\n",
              "121955  chr16:1135446:G\u003eT    chr16:611158-1659734:.  ... -0.001741      -0.737944\n",
              "\n",
              "[121956 rows x 21 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# @title Score batch of variants.\n",
        "\n",
        "# Load VCF file containing variants.\n",
        "vcf_file = 'placeholder'  # @param\n",
        "\n",
        "# We provide an example list of variants to illustrate.\n",
        "vcf_file = \"\"\"variant_id\\tCHROM\\tPOS\\tREF\\tALT\n",
        "chr3_58394738_A_T_b38\\tchr3\\t58394738\\tA\\tT\n",
        "chr8_28520_G_C_b38\\tchr8\\t28520\\tG\\tC\n",
        "chr16_636337_G_A_b38\\tchr16\\t636337\\tG\\tA\n",
        "chr16_1135446_G_T_b38\\tchr16\\t1135446\\tG\\tT\n",
        "\"\"\"\n",
        "\n",
        "vcf = pd.read_csv(StringIO(vcf_file), sep='\\t')\n",
        "\n",
        "required_columns = ['variant_id', 'CHROM', 'POS', 'REF', 'ALT']\n",
        "for column in required_columns:\n",
        "  if column not in vcf.columns:\n",
        "    raise ValueError(f'VCF file is missing required column: {column}.')\n",
        "\n",
        "organism = 'human'  # @param [\"human\", \"mouse\"] {type:\"string\"}\n",
        "\n",
        "# @markdown Specify length of sequence around variants to predict:\n",
        "sequence_length = '1MB'  # @param [\"16KB\", \"100KB\", \"500KB\", \"1MB\"] { type:\"string\" }\n",
        "sequence_length = dna_client.SUPPORTED_SEQUENCE_LENGTHS[\n",
        "    f'SEQUENCE_LENGTH_{sequence_length}'\n",
        "]\n",
        "\n",
        "# @markdown Specify which scorers to use to score your variants:\n",
        "score_rna_seq = True  # @param { type: \"boolean\"}\n",
        "score_cage = True  # @param { type: \"boolean\" }\n",
        "score_procap = True  # @param { type: \"boolean\" }\n",
        "score_atac = True  # @param { type: \"boolean\" }\n",
        "score_dnase = True  # @param { type: \"boolean\" }\n",
        "score_chip_histone = True  # @param { type: \"boolean\" }\n",
        "score_chip_tf = True  # @param { type: \"boolean\" }\n",
        "score_polyadenylation = True  # @param { type: \"boolean\" }\n",
        "score_splice_sites = True  # @param { type: \"boolean\" }\n",
        "score_splice_site_usage = True  # @param { type: \"boolean\" }\n",
        "score_splice_junctions = True  # @param { type: \"boolean\" }\n",
        "\n",
        "# @markdown Other settings:\n",
        "download_predictions = False  # @param { type: \"boolean\" }\n",
        "\n",
        "# Parse organism specification.\n",
        "organism_map = {\n",
        "    'human': dna_client.Organism.HOMO_SAPIENS,\n",
        "    'mouse': dna_client.Organism.MUS_MUSCULUS,\n",
        "}\n",
        "organism = organism_map[organism]\n",
        "\n",
        "# Parse scorer specification.\n",
        "scorer_selections = {\n",
        "    'rna_seq': score_rna_seq,\n",
        "    'cage': score_cage,\n",
        "    'procap': score_procap,\n",
        "    'atac': score_atac,\n",
        "    'dnase': score_dnase,\n",
        "    'chip_histone': score_chip_histone,\n",
        "    'chip_tf': score_chip_tf,\n",
        "    'polyadenylation': score_polyadenylation,\n",
        "    'splice_sites': score_splice_sites,\n",
        "    'splice_site_usage': score_splice_site_usage,\n",
        "    'splice_junctions': score_splice_junctions,\n",
        "}\n",
        "\n",
        "all_scorers = variant_scorers.RECOMMENDED_VARIANT_SCORERS\n",
        "selected_scorers = [\n",
        "    all_scorers[key]\n",
        "    for key in all_scorers\n",
        "    if scorer_selections.get(key.lower(), False)\n",
        "]\n",
        "\n",
        "# Remove any scorers or output types that are not supported for the chosen organism.\n",
        "unsupported_scorers = [\n",
        "    scorer\n",
        "    for scorer in selected_scorers\n",
        "    if (\n",
        "        organism.value\n",
        "        not in variant_scorers.SUPPORTED_ORGANISMS[scorer.base_variant_scorer]\n",
        "    )\n",
        "    | (\n",
        "        (scorer.requested_output == dna_client.OutputType.PROCAP)\n",
        "        \u0026 (organism == dna_client.Organism.MUS_MUSCULUS)\n",
        "    )\n",
        "]\n",
        "if len(unsupported_scorers) \u003e 0:\n",
        "  print(\n",
        "      f'Excluding {unsupported_scorers} scorers as they are not supported for'\n",
        "      f' {organism}.'\n",
        "  )\n",
        "  for unsupported_scorer in unsupported_scorers:\n",
        "    selected_scorers.remove(unsupported_scorer)\n",
        "\n",
        "\n",
        "# Score variants in the VCF file.\n",
        "results = []\n",
        "\n",
        "for i, vcf_row in tqdm(vcf.iterrows(), total=len(vcf)):\n",
        "  variant = genome.Variant(\n",
        "      chromosome=str(vcf_row.CHROM),\n",
        "      position=int(vcf_row.POS),\n",
        "      reference_bases=vcf_row.REF,\n",
        "      alternate_bases=vcf_row.ALT,\n",
        "      name=vcf_row.variant_id,\n",
        "  )\n",
        "  interval = variant.reference_interval.resize(sequence_length)\n",
        "\n",
        "  variant_scores = dna_model.score_variant(\n",
        "      interval=interval,\n",
        "      variant=variant,\n",
        "      variant_scorers=selected_scorers,\n",
        "      organism=organism,\n",
        "  )\n",
        "  results.append(variant_scores)\n",
        "\n",
        "df_scores = variant_scorers.tidy_scores(results)\n",
        "\n",
        "if download_predictions:\n",
        "  df_scores.to_csv('variant_scores.csv', index=False)\n",
        "  files.download('variant_scores.csv')\n",
        "\n",
        "df_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H_B5J8HDrA-h"
      },
      "source": [
        "Note that the resulting output dataframe could be quite large, especially if you\n",
        "use many scorers for scoring. Very large dataframes can't be filtered\n",
        "interactively, but you can interact with them using the standard `pandas`\n",
        "commands:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "height": 1574
        },
        "executionInfo": {
          "elapsed": 474,
          "status": "ok",
          "timestamp": 1753113312546,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "69iH8Chkr5XD",
        "outputId": "9287ddeb-ee32-4ff9-e35b-27a7b2b4fe31"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "repr_error": "Out of range float values are not JSON compliant: nan",
              "type": "dataframe"
            },
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/9e65f7085e7ffcb7/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n\"chr3:58394738:A\u003eT\",\n\"chr3:57870450-58919026:.\",\nnull,\nnull,\nnull,\nnull,\n{\n            'v': null,\n            'f': \"null\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        },\n\"ATAC\",\n\"CenterMaskScorer(requested_output=ATAC, width=501, aggregation_type=DIFF_LOG2_SUM)\",\n\"CL:0000084 ATAC-seq\",\n\".\",\n\"ATAC-seq\",\n\"T-cell\",\n\"primary_cell\",\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\nNaN,\nNaN,\n{\n            'v': -0.003777742385864258,\n            'f': \"-0.003777742385864258\",\n        },\n{\n            'v': -0.21573995053768158,\n            'f': \"-0.21573995053768158\",\n        }],\n [{\n            'v': 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'f': \"NaN\",\n        },\nNaN,\n\"\",\n{\n            'v': -0.000550389289855957,\n            'f': \"-0.000550389289855957\",\n        },\n{\n            'v': -0.47702011466026306,\n            'f': \"-0.47702011466026306\",\n        }],\n [{\n            'v': 3801,\n            'f': \"3801\",\n        },\n\"chr3:58394738:A\u003eT\",\n\"chr3:57870450-58919026:.\",\n\"ENSG00000213509\",\n\"PPIAP16\",\n\"processed_pseudogene\",\n\"+\",\n{\n            'v': null,\n            'f': \"null\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        },\n\"RNA_SEQ\",\n\"GeneMaskLFCScorer(requested_output=RNA_SEQ)\",\n\"CL:0000084 polyA plus RNA-seq\",\n\"+\",\n\"polyA plus RNA-seq\",\n\"T-cell\",\n\"primary_cell\",\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\nNaN,\n\"\",\n{\n            'v': 0.0004334449768066406,\n            'f': \"0.0004334449768066406\",\n        },\n{\n            'v': 0.44060030579566956,\n            'f': 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              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eCHIP_HISTONE\u003c/td\u003e\n",
              "      \u003ctd\u003eCenterMaskScorer(requested_output=CHIP_HISTONE...\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000084 Histone ChIP-seq H3K27ac\u003c/td\u003e\n",
              "      \u003ctd\u003e.\u003c/td\u003e\n",
              "      \u003ctd\u003eHistone ChIP-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eT-cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eH3K27ac\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.001469\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.373276\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e2110\u003c/th\u003e\n",
              "      \u003ctd\u003echr3:58394738:A\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr3:57870450-58919026:.\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eCHIP_HISTONE\u003c/td\u003e\n",
              "      \u003ctd\u003eCenterMaskScorer(requested_output=CHIP_HISTONE...\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000084 Histone ChIP-seq H3K27me3\u003c/td\u003e\n",
              "      \u003ctd\u003e.\u003c/td\u003e\n",
              "      \u003ctd\u003eHistone ChIP-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eT-cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eH3K27me3\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e0.001324\u003c/td\u003e\n",
              "      \u003ctd\u003e0.171333\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e2111\u003c/th\u003e\n",
              "      \u003ctd\u003echr3:58394738:A\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr3:57870450-58919026:.\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eCHIP_HISTONE\u003c/td\u003e\n",
              "      \u003ctd\u003eCenterMaskScorer(requested_output=CHIP_HISTONE...\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000084 Histone ChIP-seq H3K36me3\u003c/td\u003e\n",
              "      \u003ctd\u003e.\u003c/td\u003e\n",
              "      \u003ctd\u003eHistone ChIP-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eT-cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eH3K36me3\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.002098\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.383161\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e...\u003c/th\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e95033\u003c/th\u003e\n",
              "      \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000290756\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000290756\u003c/td\u003e\n",
              "      \u003ctd\u003elncRNA\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eRNA_SEQ\u003c/td\u003e\n",
              "      \u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000084 total RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003etotal RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eT-cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.000237\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.312224\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e95034\u003c/th\u003e\n",
              "      \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292400\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292400\u003c/td\u003e\n",
              "      \u003ctd\u003elncRNA\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eRNA_SEQ\u003c/td\u003e\n",
              "      \u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000084 total RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003etotal RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eT-cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000030\u003c/td\u003e\n",
              "      \u003ctd\u003e0.126219\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e95035\u003c/th\u003e\n",
              "      \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292423\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292423\u003c/td\u003e\n",
              "      \u003ctd\u003elncRNA\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eRNA_SEQ\u003c/td\u003e\n",
              "      \u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000084 total RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003etotal RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eT-cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000173\u003c/td\u003e\n",
              "      \u003ctd\u003e0.280661\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e95036\u003c/th\u003e\n",
              "      \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292431\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292431\u003c/td\u003e\n",
              "      \u003ctd\u003elncRNA\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eRNA_SEQ\u003c/td\u003e\n",
              "      \u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000084 total RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003etotal RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eT-cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.000035\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.126219\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e95037\u003c/th\u003e\n",
              "      \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n",
              "      \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292432\u003c/td\u003e\n",
              "      \u003ctd\u003eENSG00000292432\u003c/td\u003e\n",
              "      \u003ctd\u003elncRNA\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eNone\u003c/td\u003e\n",
              "      \u003ctd\u003eRNA_SEQ\u003c/td\u003e\n",
              "      \u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n",
              "      \u003ctd\u003eCL:0000084 total RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003e-\u003c/td\u003e\n",
              "      \u003ctd\u003etotal RNA-seq\u003c/td\u003e\n",
              "      \u003ctd\u003eT-cell\u003c/td\u003e\n",
              "      \u003ctd\u003eprimary_cell\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003eNaN\u003c/td\u003e\n",
              "      \u003ctd\u003e\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000978\u003c/td\u003e\n",
              "      \u003ctd\u003e0.635053\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/tbody\u003e\n",
              "\u003c/table\u003e\n",
              "\u003cp\u003e524 rows × 20 columns\u003c/p\u003e\n",
              "\u003c/div\u003e\n",
              "    \u003cdiv class=\"colab-df-buttons\"\u003e\n",
              "\n",
              "  \u003cdiv class=\"colab-df-container\"\u003e\n",
              "    \u003cbutton class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bfc0f1ff-31a2-4cd5-9bc7-61a30f76d72e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\"\u003e\n",
              "\n",
              "  \u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\"\u003e\n",
              "    \u003cpath d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/\u003e\n",
              "  \u003c/svg\u003e\n",
              "    \u003c/button\u003e\n",
              "\n",
              "  \u003cstyle\u003e\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  \u003c/style\u003e\n",
              "\n",
              "    \u003cscript\u003e\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bfc0f1ff-31a2-4cd5-9bc7-61a30f76d72e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bfc0f1ff-31a2-4cd5-9bc7-61a30f76d72e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    \u003c/script\u003e\n",
              "  \u003c/div\u003e\n",
              "\n",
              "\n",
              "    \u003cdiv id=\"df-044efd3c-dcfe-404a-afa2-5b4a6503aca2\"\u003e\n",
              "      \u003cbutton class=\"colab-df-quickchart\" onclick=\"quickchart('df-044efd3c-dcfe-404a-afa2-5b4a6503aca2')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\"\u003e\n",
              "\n",
              "\u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\"\u003e\n",
              "    \u003cg\u003e\n",
              "        \u003cpath d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/\u003e\n",
              "    \u003c/g\u003e\n",
              "\u003c/svg\u003e\n",
              "      \u003c/button\u003e\n",
              "\n",
              "\u003cstyle\u003e\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "\u003c/style\u003e\n",
              "\n",
              "      \u003cscript\u003e\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() =\u003e {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-044efd3c-dcfe-404a-afa2-5b4a6503aca2 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      \u003c/script\u003e\n",
              "    \u003c/div\u003e\n",
              "    \u003c/div\u003e\n",
              "  \u003c/div\u003e\n"
            ],
            "text/plain": [
              "              variant_id           scored_interval  ... raw_score quantile_score\n",
              "0      chr3:58394738:A\u003eT  chr3:57870450-58919026:.  ... -0.003778      -0.215740\n",
              "168    chr3:58394738:A\u003eT  chr3:57870450-58919026:.  ... -0.030633      -0.680793\n",
              "2109   chr3:58394738:A\u003eT  chr3:57870450-58919026:.  ... -0.001469      -0.373276\n",
              "2110   chr3:58394738:A\u003eT  chr3:57870450-58919026:.  ...  0.001324       0.171333\n",
              "2111   chr3:58394738:A\u003eT  chr3:57870450-58919026:.  ... -0.002098      -0.383161\n",
              "...                  ...                       ...  ...       ...            ...\n",
              "95033  chr16:1135446:G\u003eT    chr16:611158-1659734:.  ... -0.000237      -0.312224\n",
              "95034  chr16:1135446:G\u003eT    chr16:611158-1659734:.  ...  0.000030       0.126219\n",
              "95035  chr16:1135446:G\u003eT    chr16:611158-1659734:.  ...  0.000173       0.280661\n",
              "95036  chr16:1135446:G\u003eT    chr16:611158-1659734:.  ... -0.000035      -0.126219\n",
              "95037  chr16:1135446:G\u003eT    chr16:611158-1659734:.  ...  0.000978       0.635053\n",
              "\n",
              "[524 rows x 20 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Examine just the effects of the variants on T-cells.\n",
        "columns = [c for c in df_scores.columns if c != 'ontology_curie']\n",
        "df_scores[(df_scores['ontology_curie'] == 'CL:0000084')][columns]"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "last_runtime": {},
      "name": "batch_variant_scoring.ipynb",
      "provenance": [
        {
          "file_id": "1RzAW0xmRFe7UkS0HKCOFFV6tVkc_elUh",
          "timestamp": 1729614262382
        },
        {
          "file_id": "",
          "timestamp": 1729177232324
        },
        {
          "file_id": "1UcU771Brz691HgrtQBrA41Qrq55_GgJS",
          "timestamp": 1728927969951
        }
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
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